Fsrc-R and CT-Rct-Python-Aml-Kyc-Fraud-Manager

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EY GDS – RCT

Manager – Data, Cloud & AI Engineering

Manager – Technology Consulting 

Experience: Strictly 8+ years       

Mandatory eligibility: 

Candidates must have 8 or more years of relevant professional experience, including hands-on Python, advanced SQL, cloud, AI, and Financial Crime Compliance domain experience. Candidates below 8 years or without any mandatory capability are not eligible.

The opportunity

Lead the delivery of data, cloud, and AI-enabled Financial Crime solutions for global financial services clients. This Manager role combines technical depth with delivery leadership, stakeholder management, team development, and end-to-end ownership. The successful candidate will shape solutions across KYC, AML, Transaction Monitoring, Sanctions, and Fraud while remaining close enough to the technology to guide architecture, review critical designs, resolve delivery risks, and assure quality.

Role overview

We are seeking an experienced Manager with strong Python and advanced SQL capability, practical cloud and AI delivery experience, and broad Financial Crime Compliance knowledge. The role owns outcomes from discovery and estimation through design, build, testing, deployment, transition, and production stabilization. It also includes team leadership, work planning, governance, financial oversight, client communication, and development of reusable assets and delivery standards.

Key responsibilities

• Lead end-to-end delivery of Financial Crime technology workstreams, programs, or managed-service outcomes.
• Own solution scope, delivery plans, milestones, dependencies, risks, issues, quality, resource needs, and stakeholder commitments.
• Provide technical direction for Python applications, advanced SQL solutions, cloud architecture, data pipelines, APIs, analytics, and AI components.
• Translate KYC, AML, Transaction Monitoring, Sanctions, and Fraud requirements into scalable technical solutions and delivery roadmaps.
• Partner with compliance SMEs, product owners, architects, engineering teams, data teams, model-risk stakeholders, QA, infrastructure, and operations.
• Lead design reviews, code and SQL quality reviews, architecture decisions, defect triage, performance tuning, release readiness, and production stabilization.
• Ensure appropriate security, privacy, auditability, explainability, data lineage, model governance, monitoring, and regulatory-control considerations.
• Drive automation, reusable components, engineering standards, knowledge assets, and continuous improvement across the delivery lifecycle.
• Prepare and present status, decisions, risks, dependencies, and recovery plans to senior client and internal stakeholders.
• Support proposals, solutioning, effort estimation, staffing, capability development, and account growth activities.

Deliverable ownership

• Accountable for agreed deliverables, acceptance criteria, delivery schedule, quality, and business outcomes across assigned workstreams.
• Build and maintain integrated delivery plans covering requirements, design, data readiness, engineering, testing, deployment, transition, and support.
• Establish governance routines, delivery metrics, RAID management, change control, escalation paths, and executive reporting.
• Validate estimates, capacity, sequencing, dependencies, and release scope; act early on slippage, quality gaps, or unresolved decisions.
• Ensure traceability from business and compliance requirements to technical design, test evidence, deployment artefacts, and operational handover.
• Coordinate cross-team dependencies and secure timely sign-offs from business, compliance, technology, model-risk, security, and operations stakeholders.
• Own defect closure, production-readiness checks, go-live decisions, warranty support, and lessons learned for assigned deliveries.

Team management and leadership

• Lead multidisciplinary teams of software engineers, data engineers, analysts, QA professionals, and domain specialists.
• Define team structure, roles, goals, operating model, work allocation, and individual accountability for delivery outcomes.
• Provide regular coaching, technical mentoring, feedback, recognition, and career-development support.
• Monitor capacity, utilization, skills coverage, onboarding, knowledge transfer, succession needs, and delivery continuity.
• Foster a collaborative, inclusive, quality-focused environment with clear standards for ownership and communication.
• Resolve delivery conflicts, remove blockers, manage escalations, and support sound decisions under competing priorities.
• Identify capability gaps and create targeted upskilling plans across Python, SQL, cloud, AI, and Financial Crime domain knowledge.

Mandatory technical skills

• Python: Strong hands-on experience designing and reviewing production-grade Python solutions, including object-oriented design, data processing, APIs, testing, logging, exception handling, security, and performance optimization.
• Advanced SQL: Expert-level capability in complex joins, common table expressions, window functions, stored procedures, query optimization, large-volume analysis, reconciliation, data profiling, and investigation queries.
• Cloud: Mandatory delivery experience with Microsoft Azure, AWS, or GCP, covering solution design, deployment, data services, security, monitoring, integration, scalability, and operational support.
• AI: Mandatory practical experience delivering AI or machine-learning capabilities, including generative AI or predictive analytics, model or prompt evaluation, API integration, responsible AI controls, security, explainability, and production monitoring.
• Engineering leadership: Ability to guide architecture, review code and designs, define standards, oversee CI/CD, manage non-functional requirements, and resolve complex technical issues.
• Data and integration: Experience with data pipelines, APIs, batch processing, data quality, lineage, metadata, and integration across enterprise platforms.

Mandatory Financial Crime domain knowledge (2 or more mandatory)

• KYC / CDD / EDD: Customer onboarding, identity and document checks, customer risk rating, beneficial ownership, periodic review, remediation, and enhanced due diligence.
• AML: Financial crime risk indicators and typologies, alert and case workflows, investigations, escalation, regulatory reporting concepts, and control effectiveness.
• Transaction Monitoring (TM): Scenario and rule logic, thresholds, segmentation, alert generation, tuning, back-testing, false-positive reduction, and data-quality controls.
• Sanctions: Customer and payment screening, watchlists, potential-match handling, disposition workflows, screening effectiveness, and data quality.
• Fraud: Fraud indicators, transaction and event analysis, rules or models, alert prioritization, investigation workflows, case outcomes, and prevention controls.

AI leadership expectations

• Identify and prioritize AI use cases for alert triage, investigation support, entity resolution, anomaly detection, document analysis, quality assurance, and engineering productivity.
• Ensure human oversight, explainability, privacy, security, bias assessment, auditability, model-risk alignment, and appropriate use of regulated data.
• Define measurable acceptance criteria and monitoring for accuracy, grounding, latency, reliability, operational risk, and business value.
• Use AI-assisted engineering tools responsibly for code, testing, documentation, and analysis while maintaining review and security controls.

Good to have

• Actimize: Experience delivering, enhancing, integrating, or supporting NICE Actimize solutions for Transaction Monitoring, AML, sanctions, fraud, alerting, or case management.
• SAS: Experience with SAS programming, SAS AML, SAS Fraud Management, analytics workflows, data preparation, or migration and modernization initiatives.
• Exposure to Spark, Databricks, Kafka, Docker, Kubernetes, Terraform, Airflow, Azure Data Factory, or equivalent modern data and DevOps tooling.
• Experience supporting proposals, commercial estimates, statements of work, delivery financials, and senior client presentations.

Experience and education

• Experience: Strictly 8+ years of relevant professional experience in technology delivery, software or data engineering, cloud, AI, analytics, or Financial Crime Technology.
• Leadership: Demonstrated ownership of complex deliverables and experience managing multidisciplinary teams and senior stakeholders.
• Mandatory combination: Python, advanced SQL, cloud, AI, and practical experience across the Financial Crime Compliance domain are required.
• Education: Bachelor’s or Master’s degree in Computer Science, Engineering, Information Technology, Data Science, or a related field.

What success looks like

• Predictable delivery with clear ownership, transparent governance, controlled risk, strong quality, and timely stakeholder decisions.
• Scalable Python, SQL, cloud, and AI solutions aligned to Financial Crime controls and operational needs.
• A high-performing team with clear accountability, stronger capability, and sustainable delivery practices.
• Client confidence built through technical credibility, concise communication, proactive escalation, and measurable outcomes.

What working at EY offers

You will lead meaningful Financial Crime Technology assignments in an interdisciplinary environment that values quality, practical learning, collaboration, responsible innovation, and knowledge exchange. The role provides opportunities to shape client outcomes while growing teams and capability across data engineering, cloud, AI, and Financial Crime Compliance.

Apply only if you meet the 8+ years requirement and all mandatory technical, leadership, and domain criteria.

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